13.6 Glocal Memory 265
— but this subnetwork still might occupy only a small fraction of the neurons in the brain (say, 1/100 or less), since there are very many neurons available. This attractor would constitute the map. But then, there would be a much smaller number of neurons serving as key to unlock this map: i.e. if a few of these key neurons were stimulated, then the overall attractor pattern in the map as a whole would unfold and come to play a significant role in the overall brain activity landscape. In prior publications [Goe97] the primary author explored this hypothesis in more detail in terms of the known architecture of the cortex and the mathematics of complex dynamical attractors.
So, one possible interpretation of the [QKIXF08] data is that the MTL neurons they’re measuring are part of key networks that correspond to broader map networks recording percepts. The map networks might then extend more broadly throughout the brain, beyond the MTL and into other perceptual and cognitive areas of cortex. Furthermore, in this case, if some MTL key neurons were removed, the maps might well regenerate the missing keys (as would happen e.g. in the glocal Hopfield model to be discussed in the following section).
Related and interesting evidence for glocal memory in the brain comes from a recent study of semantic memory, illustrated in Figure ?? [PNRO7]. Their research probed the architecture of semantic memory via comparing patients suffering from semantic dementia (SD) with patients suffering from three other neuropathologies, and found reasonably convincing evidence for what they call a “distributed-plus-hub” view of memory.
The SD patients they studied displayed highly distinctive symptomology; for instance, their vocabularies and knowledge of the properties of everyday objects were strongly impaired, whereas their memories of recent events and other cognitive capacities remain perfectly in- tact. These patients also showed highly distinctive patterns of brain damage: focal brain lesions in their anterior temporal lobes (ATL), unlike the other patients who had either less severe or more widely distributed damage in their ATLs. This led [PNR07] to conclude that the ATL (being adjacent to the amygdala and limbic systems that process reward and emotion; and the anterior parts of the medial temporal lobe memory system, which processes episodic memory) is a “hub” for amodal semantic memory, drawing general semantic information from episodic memories based on emotional salience.
So, in this view, the memory of something like a “banana” would contain a distributed as- pect, spanning multiple brain systems, and also a localized aspect, centralized in the ATL. The distributed aspect would likely contain information on various particular aspects of ba- nanas, including their sights, smells, and touches, the emotions they evoke, and the goals and motivations they relate to. The distributed and localized aspects would influence one another dynamically, but, the data [PNR07| gathered do not address dynamics and they don’t venture hypotheses in this direction.
There is a relationship between the “distributed-plus-hub” view and [Dam00] better-known notion of a “convergence zone”, defined roughly as a location where the brain binds features to- gether. A convergence zone, in [Dam00] perspective, is not a “store” of information but an agent capable of decoding a signal (and of reconstructing information). He also uses the metaphor that convergence zones behave like indexes drawing information from other areas of the brain — but they are dynamic rather than static indices, containing the instructions needed to recognize and combine the features constituting the memory of something. The mechanism involved in the distributed-plus-hub model is similar to a convergence zone, but with the important differ- ence that hubs are less local: [PNRO7] semantic hub may be thought of a kind of “cluster of convergence zones” consisting of a network of convergence zones for various semantic memories.
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